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Inflow and outflow stenoses screening on biophysical experimental arteriovenous graft using big spectral data and bidirectional associative memory machine learning model

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Inflow and outflow stenoses screening on biophysical experimental arteriovenous graft using big spectral data and bidirectional associative memory machine learning model

Content Provider	IET Digital Library
Author	Lin, Chia Hung Chen, Wei Kan, Chung Dann
Abstract	Long-term repeating traumatic puncture is required for dialysis therapy, which results in frequent thrombosis and graduate vascular access stenosis, such as inflow or outflow stenosis and coexistence of both. An arteriovenous graft has a higher patency rate than an arteriovenous fistula. This study intends to use the dual-channel auscultation-based non-invasive method to screen inflow and outflow stenoses. Frequency analysis is used to decompose phonoangiography (PAG) signals to frequency features using the different data length of acoustic data. Burg autoregressive method is employed to extract the key frequency parameters from sufficient spectral data, including characteristic frequencies and distinct peaks of power spectral densities (PSDs). In big data processing, PSDs and the degree of stenosis (DOS) have been validated to show a positive correlation with sufficient big spectral data. An intelligent machine learning model, bidirectional hetero-associative memory network (BHAMN), is carried out to identify the level of DOS at the inflow site, the mid-site, or the outflow site of a vascular access. The experimental results will indicate that the proposed intelligent machine learning model has higher hit rates.
Starting Page	139
Ending Page	147
Page Count	9
Volume Number	4
e-ISSN	23983396
Issue Number	Issue 2, Jun (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cps/4/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cps.2018.5030
Journal	IET Cyber-Physical Systems: Theory & Applications
Publisher	The Institution of Engineering and Technology
Publisher Date	2018-10-19
Access Restriction	Open
Rights License	Creative Commons Attribution-Non Commercial-No Derivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)
Subject Keyword	Acoustic Data Arteriovenous Fistula Autoregressive Processes Bidirectional Associative Memory Machine Bidirectional Hetero-associative Memory Network Big Data Processing Big Spectral Data Bioacoustics Biology And Medical Computing Biomedical Engineering Biophysical Experimental Arteriovenous Graft Blood Vessels Burg Autoregressive Method Characteristic Frequencies Content-addressable Storage Data Length Dialysis Therapy Digital Signal Processing Diseases Dual-channel Auscultation-based Noninvasive Method Frequency Analysis Frequency Feature Frequent Thrombosis Graduate Vascular Access Stenosis Haemodynamics Inflow Site Intelligent Machine Learning Model Key Frequency Parameter Knowledge Engineering Technique Learning in AI Long-term Repeating Traumatic Puncture Medical Signal Processing Outflow Site Outflow Stenoses Patency Rate Patient Care And Treatment Patient Treatment Phonoangiography Signal Pneumodynamics Power Spectral Densities Signal Processing And Detection Spectral Analysis Statistics
Content Type	Text
Resource Type	Article

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